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feat: remove unnecessary prints
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@ -173,7 +173,6 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendati
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This will choose a random anime name and our prediction_model will predict similar anime.
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"""
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np.random.seed(seed)
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print(pivot_table)
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if anime == "RANDOM":
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chosen_anime = np.random.choice(pivot_table.shape[0])
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query = pivot_table.iloc[chosen_anime, :].values.reshape(1, -1)
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@ -316,7 +315,6 @@ def auto_mode():
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preprocess_model_predict(
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starting_rating_data, starting_anime_contact_data, starting_rows_number, original_pivot_table, metric=metric)
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for algorithm in algorithm_spread:
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print("automode, algorithm_spread")
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for metric in sorted(VALID_METRICS_SPARSE[algorithm]):
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preprocess_model_predict(
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starting_rating_data, starting_anime_contact_data, starting_rows_number, original_pivot_table, algorithm=algorithm)
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